Environ. Sci. Techno/. 1995, 29, 647-656
Measurement Error and Snatial Variability Effects on Characterization of Volatile Organics in the Subsurface -
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OLIVIA R. W E S T , * f t ROBERT L. SIEGRIST,I TOBY J. MITCHELL,* AND ROGER A. J E N K I N S s Environmental Sciences Division, Engineering Physics & Mathematics Division, and Chemical Sciences Division, Oak Ridge National Laboratory, P.O.Box 2008, Oak Ridge, Tennessee 37831 -6036
The effects of measurement error and spatial variability on establishing subsurface contaminant distributions were demonstrated in a study described herein, where soil underlying a former land treatment facility was intensively sampled and analyzed for volatile organic compounds (VOCs). Concentrations of VOCs measured on-site using a heated headspace/ gas chromatography method were typically 10 times higher than concentrations measured at an offsite laboratory using a purge-and-trap/gas chromatography/mass spectrometry method. This was attributed either to VOC losses during storage and preparation of off-site samples and/or inefficient VOC extraction in the off-site method. Three contaminant distribution models developed from the on-site VOC measurements were evaluated through cross-validation and subregion sampling methods. Order of magnitude discrepancies existed between predicted and measured concentrations. These results show that increasing sampling density with cost-effective field analyses can be more effective than using complexspatial models to overcome the lack of spatial information in sparse data sets comprised of off-site laboratory analyses.
Introduction Volatile organic compounds (VOCs) are the most prevalent class of compounds found in soils underlying contaminated sites throughout the industrialized world (1-3). In order to assess the environmental risk posed by soil contamination andlor to develop restoration strategies, the distribution of subsurface VOC concentrations at these sites must be established. For this purpose, VOC levels are measured in a discrete number of soil samples collected from the site. Based on these discrete VOC measurements, spatial models for contaminant distribution can be developed either through simple, two-dimensional contouring of concentrations at fixed depths or more rigorous threedimensional interpolation procedures (4). The accurate characterization of VOC spatial distributions depends on the accuracy of the discretemeasurements and the density of their locations at the site. Traditionally, a limited number of soil samples are collected, and VOC analyses are made in off-site laboratories at relatively high cost (e.g., $300 per sample). The results of this limited number of analyses are used to infer concentrations present within a volume of soil that can be 8 orders of magnitude larger than the combined volumes of the soil samples. A larger number of measurements in denser sampling grids is possible if less expensive field VOC analysis techniques are employed. Unfortunately, analyses made on-site using field methods have generally been considered less accurate and suitable only for screening purposes (1, 5, 6). This is further illustrated by the required verification of field measurements through conventional laboratory analysis (6, 7). Yet, research has shown that field methods can yield equally or more accurate results by minimizing VOC losses than can occur during preanalytical storage of samples shipped to an off-site laboratory (7-9). This paper describes a study wherein a former land treatment facility for waste oils and solventswas intensively sampled. Analyses of soilVOCswere conducted using both on-site and off-sitemethods, and the results were compared (10). Three-dimensional models for the spatial distribution of VOCs were developed from a relatively dense set of measurements, and their comparative accuracy in predicting VOC concentrations was evaluated.
Methods Site Description. The study site was a former land treatment unit (-37 m wide by 81 m long) that had been used for the disposal of waste oils and solvents from 1976 to 1983 (10). Located in southern Ohio, the site is underlain by a 9-m-thick layer of fine-grained deposits (Minford silts and clays: 90% of particles 150 pm; water content = 1624 drywt %; total organic carbon content = 184-1190 mg/ kg dry soil) with a permanent water table at a depth of 3.6-4.3 m (see Figure 1). Early characterization studies revealed subsurface contamination principally within the Minford clay and silt layers by chlorinated aliphatic hydrocarbons like trichloroethylene (TCE), with low levels * Corresponding author; e-mail address:
[email protected] +
Environmental Sciences Division.
* Engineering Physics & Mathematics Division. 5
0013-936X/95/0929-0647$09.00/0
D 1995 American Chemical Society
Chemical Sciences Division.
VOL. 29, NO. 3, 1995 /ENVIRONMENTAL SCIENCE &TECHNOLOGY
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January I992 sampling i ntenrals \
April 1992 y m p l i n g intervals
9m
I 1.2 m
A
FIGURE 1. Shallow lithology underlying the land disposal site and soil sampling intervals.
of heavy metals and radionuclides. Contamination due to residual semivolatile (SVOC) or petroleum organic compounds was ruled out by nondetectable SVOC analysis results and total organic carbon contents less than 0.1%by weight (10). Soil Sample Collection. The VOC data sets in this study were generated during two separate sampling events. In January 1992, measurements were made at a total of 176 locations within a 19 OOO-m3soil region underlying the site. At each of 21 sampling locations spaced -10 m apart, soil cores were collected at eight 30-cm-long depth intervals fromgroundsurfaceto 6.6m (0-0.3,O.g-1.2, 1.8-2.1,2.73.0,3.6-3.9,4.5-4.8,5.4-5.7, and6.3-6.6 m, Figure 1).In April 1992,a second set of samples were collected from 204 locations within a smaller subregion (-10 m wide by 36 m long) representing approximately 10%of the volume of the region sampled in January 1992. Soil cores were taken from four depth intervals (0.3-0.6, 1.2-1.5, 2.4-2.7, and 3.94.2 m, Figure 1) at 42 locations spaced 1-5 m apart. A hydraulic probe sampler (Geoprobe) was used to collect intact soil cores in 30-cm-long, 2.5-cm diameter thin-walled steel tubes. Use of the hydraulic probe resulted in less subsurface disturbance and more rapid sampling when compared to other drilling methods (1). Immediately after sampling from a given depth interval, soil contained in the steel tube (-100 g) was extruded into a polyethylene bag using the same hydraulic mechanism that drives the sampler tubes into the ground. Immediately after extrusion, 10-20-g subsamples were collected from the polyethylene bag using a subcoring device (Associated Design and Manufacturing Co., ref 1) and placed into containers appropriate for the VOC analysis method intended for the subsamples (see below). Extrusion and subsampling were 648 a ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 29. NO. 3.1995
conducted as rapidly as possible ('2 min for the entire process) in order to minimize VOC losses. Soil VOC Analyses. At all sampling locations in both the January and April 1992 sampling events, subsamples were analyzed on-site for VOCs within 24 h of collection using a heated headspace technique (HHS) (11). These subsamples were collected as described above and contained in 40-mLglassvials with Teflon-linedsilicone septum caps. In the HHS technique, the 40-mL vial containing the soil sample is heated to 60 "C in a water bath for 230 min. A sample of headspace is then withdrawn in a syringe and injected into a Shimadzu 14A gas chromatograph (GC) equipped with a 30-m Restek Rtx-volatiles megabore capillary column and an electron capture detector (ECD). The GClECD was calibrated to quantify seven chlorinated aliphatic compounds known to be predominant at the site: trichloroethylene (TCE); 1,l,l-trichloroethane (TCA); 1,ldichloroethylene (1,l-DCE);1,l-dichloroethane (DCA);cis1,2-dichloroethylene(c-1,2-DCA);trans- 1,2-dichlorothyIene (t-1,2-DCE);and methylene chloride (MC). The headspace concentration was quantified through a four-point calibration curve constructed from 50-, 300-, 500-, and 2000-ng headspace standards. Soil VOC concentration was then deduced from the mass of field moist soil in the vial and the headspace concentration, assuming that sample heating at 60'C for at least 30 min had caused the VOCs to entirely volatilize from the mineral soil into the sample headspace. At 12 of the sampling locations in the January 1992 sampling event, two separate subsamples were collected from the same 30-cm core and analyzed for VOCs using the heated headspace technique. These sample duplicates were used to quantify short-range spatial variability (Le., variability of VOC concentrations within a 30-cm core).
TABLE 1
Summary of Target Compound Concentrations Determined in Soil Samples Collected and Analyzed On=Site during Januaty 1992 Sampling Event at Land Disposal Site in Ohioe statistic
no. of measurements
TCE
187 2126 7046 331
av soil concn (pglkg) SD coeff of variation (%) 0 min soil concn (pglkg) max soil concn (pglkg) 20000 correlation of compd with summationC 0.91
l,l,l-TCA
187 292 600 206
MC
1,l-DCE
l,P-DCEb 1,l-DCA
187 187 187 2392 535 274 9173 1661 1216 384 310 443 0 2 1 2 4200 40000 14000 7808 0.59 0.95 0.02 0.15
187 25 37 144 1 130 0.67
summation (total VOCs)
187 5644 15727 279 9 64014 1 .oo
a For this analysis, nondetects were set equal to the reported detection limit. Results are reported on the basis of field moist soil weight. Summation of cis- and trans-1,2-DCE isomers. Pearson correlation coefficient, r.
Separate subsamples were collected from 20% of the total number of sampling locations and sent to an off-site laboratory for VOC analysis according to procedures for low-levelcontamination outlined in EPA Method 8260 (Le., purge-and-trap extraction followed by analysis on a gas chromatograph/massspectrometer, PT-GUMS) (12). These samples (10-20 g) were‘collected in the same manner as the samples for on-site analysis, but were placed into 40mL Dynatech vials (Dynatech Precision, Inc.). These vials have two threaded ends and septum caps which allow them to be used as purge-and-trap vessels on a modified Tekmar purge-and-trap concentrator. In this modified purge-andtrap system, purge gas was injected into the Dynatech vial through a fitting attached onto the vial’s bottom threaded end. The purge gas was then collected through a fitting attached onto the vial’s top threaded end. A glass frit close to the bottom end of the vial supports the soil sample during purging as well as enhances extraction by diffusingthe purge gas. Both threaded ends of the Dynatech vials were sealed with Teflon-lined silicone septum caps during sample shipment. These caps had to be removed prior to installation of the vial on the purge-and-trap concentrator. Removal of these caps and attachment of the purge-andtrap fittings were performed rapidly to minimizeVOC losses (-5 s).
In addition to the heated headspace and purge-andtrap samples, separate soil subsamples from 12 sampling locations were collected and immersed in methanol during the April 1992 sampling event. These samples (10-20 g) were collected in the same manner as the samples for onsite and purge-and-trap analysis, but were placed into 40mL vials with Teflon-lined silicone septum caps prefilled with 10 mL of reagent-grade methanol. These methanolimmersed samples were analyzed at an off-site laboratory following procedures for high-level contamination also outlined in EPAMethod 8260 (i.e.,purge-and-trap extraction of a methanol aliquot followed by GUMS analysis,MeOHGUMS) (12). Multiple subsamples taken from the same depth interval but intended for different VOC analysis methods were collected in random order. Therefore,comparisons ofVOC levels in subsamples collected from the same interval but analyzed using different methods are not expected to be biased by the subsampling sequence. Samples for on-site analysis were delivered to the mobile laboratory immediately after collection and kept at -4 “C until the VOC analysis was made, which occurred in all cases within 24 h of sample collection. Samples for off-site analyses were placed in coolers at -4 “C and shipped to an analytical laboratory where they were transferred to a refrigerator
maintained at -4 “C pending analysis, which occurred within 14 days of collection. Spatial Modeling of VOC Distributions. The spatial modeling described here was aimed at seeking a function
u(x,y,z)= log(total VOC concentration)
(1)
where x,y,z are spatial coordinates, which best represented the data set consisting of VOC concentrations measured on-site in soil samples collected during the January 1992 sampling event. Three interpolationlsmoothing methods were used to develop the VOC spatial models. Method I was a commercially available three-dimensional interpolator based on the contouring method of Briggs (13). The method searches for an interpolating function that has the minimum total curvature over a grid that covers the region of interest. This method is a “strict interpolator” since the values of the interpolating function at the sampled locations are equal to the measured values. Method I1 was a general thin-plate spline smoothing method developed by Wahba and Wendelberger (14, 15) and implemented in the public domain sofnvare RKPACK (16). This smoothing method seeks an interpolating function that compromises between minimizing total curvature and minimizing residual sum of squares. The interpolating function derived using this method is less sensitive to noise in the data and to the occurrence of random high-level or low-level spots when compared to astrict interpolator. Method I11was a Bayesian prediction method similar to 3 D kriging (17). This method is based on viewing the VOC interpolating function as a sum of three components: (a) a smooth, gradugly varying function that captures global behavior (much like a linear or quadratic response surface);(b) a much rougher function that captures short-range variability; and (c) random “noise”,which represents variability between two samples taken from the same location (Le.,samples collected from the same 30-cm interval have the same spatial coordinates). Each of these components is treated as a realization of an appropriately chosen random function. Adjustable parameters of these functions were determined by the method of maximum likelihood (18). To evaluate the three spatial prediction methods, two cross-validation exercises were conducted using the VOC data set from the January 1992 sampling event. In the first cross-validationexercise, theVOC data at three depths (Le., 0.9-1.2,2.7-3.0, and 4.5-4.8 m) were excluded from each of 21 borings. The remaining samples from five depths were used to derive the interpolating function. The predicted VOC values at these excluded locations were then VOL. 29, NO. 3, 1995 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
649
0 A
First VOC data set Second VOC data set
1000000 100000
10000
I000 100
10 1
1
10 100 1000 10000 100000 Field-measured trichloroethylene concentration ( u g k g )
1 000000
FIGURE 2. Comparison between VOC levels measured using heated headspace (field or on-site) and purge-and-trap (lab or off-site) methods.
comparedwith the observed values (thosethat were actually measured), and the errors of prediction were computed. The second exercise was carried out as in the first, except that the excluded data set consisted of all samples collected from four of the 21 boring locations. The remaining samples (total of 143) were used to predict the VOC concentrations in the excluded set. In the second exercise, a twodimensional kriging program was also used (SURFER, Golden Graphics Corp., Golden, CO). This 2-D kriging method interpolates within each of the eight depths separately. In addition to the cross-validation exercises, the predictive capability of the spatial models was further evaluated by comparing predicted and measured VOC concentrations at each of the 204 sampling locations in the second data set.
compared to corresponding PT-GUMS measurements, as shown by the large number of points beneath the 1:l line in Figure 2. The statistical significance of this observation was verified by a t-test for paired data, with each pair consisting of HHS and PT-GCIMS measurements of TCE concentrations on samples taken from the same 30-cm interval. Log-transformed TCE concentrations were used in the statistical analysis since both data sets were found to be log-normally distributed. The paired t-test showed that the HHS-measuredTCE concentrations were different at a 5% significance level from the values obtained by PTGUMS values. In order to estimate the discrepancy between the HHS and PT-GUMS measurements, the following difference was calculated for each data pair:
Results VOC Concentrations. Based on the results of the on-site analyses for seven chlorinated aliphatic compounds, TCE and MC were predominant with average concentrations of 2126 and 2392pg/kg, respectively (Table 1). A "total VOC" concentration, equal to the sum of the concentrations of the seven target compounds, was also calculated for each soil sample (see Table 1). Concentrations of TCE and MC were very well correlated with the totalVOC concentration, having correlation coefficients of 0.91 and 0.95, respectively. This suggested that trends in the total VOC distribution were similar to the trends in the distribution of the predominant compounds and that spatial models of total VOCs could be used in lieu of compound-specific spatial models (e.g., for TCE and MC). Comparison of VOC Analysis Results. TCE concentrations were predominantlyhigher in the HHS samples when 650 ENVIRONMENTAL SCIENCE &TECHNOLOGY / VOL. 29, NO. 3, 1995
where CTCE,HHS and CTCE,FTCG/MS are TCE concentrations measured by the heated headspace and purge-and-trap methods, respectively. The average A with 95%confidence intervals for the January 1992 andApril/May 1992 sampling eventswere0.97&0.25and 1.17f0.31,respectively. These results indicate that, on average, the TCE concentration measured on-site by the heated headspace technique is at least an order of magnitude larger than the corresponding measurements using the off-site PT-GCIMS method. Concentrationsof MC as measured on-site ranged from 10 to 12 000 pglkg while off-site measurements were all below the detection limit of 5 pg/kg. Differences between the on-site and off-sitemeasurements of 1,l-DCE,l,Z-DCE, l,l-DCA, and l,l,l-TCAwerenotstatisticallysignificant.It
+MeOH-GUMS
Heated Headspace ----a- PT-GC/MS 00000
1
.--I
.
.
. ..
..
r . !
-
.-
...I ...
10000
1000
100
10
1
1
2
3
4
5 6 7 8 Sample Number
9 1 0 1 1 1 2
FIGURE 3. Comparison among bichlomethylene levels measured in collocated silty clay soil samples by using on-site heated headspace GCFCO analysis, off-site purge-and-trap GC/MS analysis, and off-site purge-and-trap GClMS analysis of methanol-immersedsamples.
should be noted however that these compounds were detected at relatively low levels when compared to TCE and MC (see Table 1). Samples collected duringAprill992 enabledcomparison among field measurements using the heated headspace techniques (HHS), using laboratory VOC measurements bydirectpurge-and-trap GC/MSanalysisofthesoilsamples in the Dymatech vials (PT-GC/MS), and using methanol extraction followed by purge-and-trap analysis of methanol extracts (MeOH-GCIMS). TCE concentrations determined byeachofthethreemethodswerecomparedat 12 sampling locations wherein three separate soil samples were collected, and each was analyzed using a different technique (see Figure 3). In general, VOC levels in corresponding HHS andMeOH-GUMS samples were differentby anorder of magnitude. Whenever HHS concentrations were lower than 1000 pg/kg, MeOH-GUMS levels were consistently higher. However, this trend is reversed at higher HHS concentrations (see Figure 3). Due to this trend reversal, a t-test for paired data at a 5% significance level did not detect a significant difference between TCE levels from the HHS and MeOH-GC/MS methods. On the other hand, a significantdifferencebetween PT-GUMS and MeOH-GCI MS TCE levels was detected at the 5% significance level. Using an equation analogous to eq 2, differences were calculated for paired TCE levels from the MeOH-GC/MS and PT-GUMS methods. The average differencewith 95% confidence limits is 1.16 % 0.59, indicating that TCE levels
TABLE 2
Total VOC Concentrations in Duplicate Soil Samples Taken from Same 30-cm Sampling lntewala higher VOC pmbe
depth
no.
lltl
GPO3 9-10 GPO3 12-13 wnd
X - - L.
GPO8 GPO9 GPO9 GP14 GP14 GP15 GP16 GP16 GP24
21-22 3-4 21-22 18-19 21-22 12-13 3-4 12-13 15-16
lower VOC pair kglkgl
ratio of higherto lower concn
2792 571 700 181 531 174 784 5290 4725 152 50 477
1.50 6.99 1.19 1.91 1.77 1.71 2.36 1.28 2.99 8.71 2.24 1.09
concn duplicate concn in duplicate
pair Ipgikgl 4195 3992 830 ..~ 345 938 298 1848 6760 14117 1324 112 520
a Nondetects were excluded from the summation of TCE, MC, TCA, 1.1-DCE. 1.2-DCE (sum of cis- and trans-1.2-DCE isomersl, and 1.1DCA. Results are reponed on the basis of field moist soil weight.
were an order of magnitude higher in methanol-immersed soil samples. Concentrations of other target compounds in the methanol-immersed samples were predominantly below the detection limit of the MeOH-GCIMS method (250pgl Thus, a comparison of results from the three methods for these other compounds could not be conducted.
e).
VOL. 29. NO. 3,1995 iENVIRONMENTAL SCIENCE &TECHNOLOGY m 651
Total VOC Distribution (Method I: Strict Interpolator)
.'
(0 G5.C
c
350
a
4
lZt.0 m.0 0.0 6110
-/si
1l.C
FIGURE 4. Three-dimensional visualization of the total VOC imerpolatian function obtained using method I.
Spatial Variability. The results of HHS concentration measurements made at arelatively large number of sample locations revealed a heteroeeneous distribution of VOCs. indicated by coefficients o f k i a t i o n that exceeded 200% for all target compounds (Table 1). Contributing to this variability were long-range spatial trends in VOC concentrations, with relatively higher levels near ground surface at the center of the site decreasing with depth below the surface and distance toward the edges of the site. In addition to these long-range spatial trends, short-range spatial variability was high. Significant differences were observed between HKS VOC measurements made on 12 pairs of soil samples collected from the same 30-cm-long sampling interval [Table 21. Tkis shoa-range spatial variability can be quantified by the standard deviation of VOC measurements taken from the same location [i.e., withinthesame 30-cmsamplingintervall. Usingthe paired measurements at the 12 sample locations, a standard deviationof0.311wasestimatedforlog(VOC1duetoshortrange variability within 30 cm. Assuming that 95% of the log(V0C) values within a 30-cm interval fall within 2 standard deviations of the mean [Le., ~ I . , W C ~ k 0.6221, a variability of more than 1order of magnitude within a 30cm interval due to spatial heterogeneity is not unusual. Daerences between paired measurements due to analytical
a?.. ENVIRONMENTAL SCIENCE &TECHNOLOGY I VOL. 29. NO. 3.1995
variability was a small component of the total variance. Analyses ofduplicate samples collectedfromthe headspace of each of nine 40-mL vials showed an estimated standard deviation for log(VOC1 of only 0.0115, more than 1 order ofmagnitude smallerthan0.311 forduplicatesoilsamples. Spatial Modeling and Visualization. Visualizations of the interpolating functions u[x,y,z) determined from the on-site data using modeling methods I and I11 (Figures 4 and 51 show differences in smoothness of the representations but with two consistent impressions: (a1 higher VOC concentrations exist nearer the surface and [b)higher VOC concentrations exist near the middle of the eastern part of the region. The appearance of high-level and low-level spots in the visualizations should be interpreted with caution since these invariablyappear at sampled locations. Because of the considerable short-range variability described above, it is difficult to predict whether very high or very low contaminant levels exist at unsampled locations. In general, the prediction at an unsampled location was an unremarkable intermediate value. The prediction errors determined during the two crossvalidation exercises are shown in the boxplots in Figure 6. It is evident that there is not much difference among the apparent prediction errors of the three different spatial modeling methods. No substantial differences can be
(Method 111: Krighg)
FIGURE 5. Three-dimensional visualization of the total VOC interpolation function obtained using method 111 three-dimensional krigingl.
detected among the performance of the three-spatial modeling methods when used to predict the VOC concentrations at the locations sampled during April 1992 (Figure 7). The height of each box in Figure 7 (which contains the middle 50%ofthe errors) is virtually the same for each method, although there is a slight difference in their locations. A userul property of the kriging methodolohy (mcihod 111) is that ii orovides csiimaies of unrrriaintv with each prediction. Figure 8 shows the measured VOC concentrationsintheApril 1992datasetrelativetothe95Wprediction intervals based on the January 1992 data set. Even though these intervals are wide (the upper limit is 200-300 times the lower limit), they still failed to cover 28 of the 204 observed values. In nearly all of these cases, the observed value washigher than expected. This clearly demonstrates that the prediction of concentrations at discrete points within a region of interest is very imprecise.
Discussion This work illustrates the difficulties in quantifymg VOC contamination in the subsurface, both in the sampling and analysis phase as well as in the subsequent interpretation of the data. The VOC measurements using a field-based
'
method were found to he consistently higher than corresponding measurements in samples sent to an off-site laboratory. This may have been due to VOC losses during storage, transport, and preparation of the PT-GUMS samples (10, 11. 19-21). Since threaded caps are not necessarily leak-proof and Teflon is not impermeable to VOCs (22).analvtes could have heenlost from the container duringpreanalyrical sdmpk holding. Mortwver.even brief opening of rhe Dynarrrh vials prior io analysis could have resulted in vapor-phase VOCs escaping from the vial. ReducedVOC losses could have been achieved by using an autosampler that eliminates the need for opening the vials prior to analysis. Unfortunately, this autosampler was not available at the off-site laboratory where the samples were analyzed. Differences between VOC extraction efficiencies in the PT-GUMS and HHS methods may he another reason behind differences between on-site and off-site analysis results. Hewitt et al. (23)compared ITGCIMS analysis to a static headspace technique wherein water was added to the soil sample prior to headspace analysis. Samples were not heated, and method calibration was based on headspace analysis of aqueous standards. They concluded that the two methods gavecomparableresults. Voice andKolb (24) VOL. 29. NO. 3.1995 I ENVIRONMENTAL SCIENCE &TECHNOLOGY.
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FIGURE 6. Cross-validation results for spatial models using the VOC data set for January 1992 and excluding sample depths or probe locations. (Note: The line in the box represents the median ratio while the tops and bottoms indicate *25% of the computed ratios. the vettical lines mark the range of ratios. and the circles represent outliers.)
2
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